CN111882368B - On-line advertisement DPI encryption buried point and transparent transmission tracking method - Google Patents
On-line advertisement DPI encryption buried point and transparent transmission tracking method Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention discloses an on-line advertisement DPI encryption buried point and a transparent transmission tracking method, which can realize accurate identification and tracking of advertisement user individuals on the premise of fully ensuring data safety and legal regulations, and open user data and other labels with different dimensions in an operator data capacity open platform for deep analysis, and continuously optimize and finish a more accurate user advertisement orientation algorithm. The invention provides a method for encrypting buried points and transparent tracking of an online advertisement DPI, which provides a safe and accurate advertisement monitoring method, and can perform data communication and deep analysis on users who perform specific advertisement browsing, clicking, converting and other actions on the premise of legal and compliance. The invention can greatly expand the application value and space of on-line advertisement buried points and subsequent data analysis, and solves the problems that the prior art cannot accurately orient advertisement behavior users, cannot communicate with other user characteristic data and cannot process deeper analysis demands.
Description
Technical Field
The invention relates to the technical field of internet information processing, in particular to an on-line advertisement DPI encryption buried point and a transparent transmission tracking method, and more particularly relates to a method for identifying and tracking advertisement click users by combining with telecom operator deep data packet detection log data.
Background
With the advent of big data and artificial intelligence, advertising industry has also presented new development trend, and the behavior change at the individual level has gradually become the core of advertising effect; the advertising campaign is also "diverted" from being "media value centric" to being user centric. The accurate advertisement operation mode needs to directly face the user data, and the media will gradually become the carrier of the user data. One party is advertisement content and characteristic data, the other party is user attribute and behavior data, and the aim of accurate advertisement operation is to match specific advertisements with specific users. Only to the extent that each advertisement and each user is accurate, stakeholders evaluate the effectiveness of the advertisement will tend to be direct and accurate.
The existing online advertisement spot burying method is limited by a browser, so that only part of identification information such as IP addresses and Cookies of users can be obtained, and more accurate identity orientation cannot be performed on the users. The method makes the subsequent data analysis become isolated, has relatively limited application value, and is not more convenient to mine other correlations for other characteristic data of the user by subsequent communication with other data sources. The traditional method can not accurately identify the individual user, so that basic advertisement data analysis can be generally only performed, and deeper analysis requirements can not be processed.
In view of the above problems, there is a need to design a method for solving the problems that the prior art cannot accurately orient the advertisement behavior user, cannot open up with other user characteristic data, and cannot deal with deeper analysis requirements.
Disclosure of Invention
Aiming at the defects, the technical problem solved by the invention is to provide an on-line advertisement DPI encryption buried point and transparent transmission tracking method, so as to solve the problems that the prior art cannot accurately orient advertisement behavior users, cannot be communicated with other user characteristic data and cannot process deeper analysis requirements.
The invention provides a method for encrypting buried points and transparent tracking of an online advertisement DPI, which comprises the following specific steps:
step 1, accessing an SDK in an advertisement page, and collecting user operation behaviors, wherein the user operation behaviors comprise page access times, page stay time and button click times;
step 2, the core code globally monitors the HTML tag, and when a user enters an advertisement page to perform various operations, a corresponding monitoring statistical mechanism is triggered to obtain relevant operation statistical data of the user;
step 3, encrypting the returned statistical data, and simultaneously encrypting and transmitting the statistical data to an operator DPI analysis log;
step 4, carrying out multidimensional user advertisement browsing behavior aggregation analysis on the returned statistical data, and analyzing the encrypted and transmitted data to generate a multidimensional user behavior wide table;
step 5, data open reduction is carried out by combining the multidimensional user behavior broad table with the result of aggregation analysis;
and 6, carrying out subsequent delivery analysis by using a Flink stream type calculation engine in combination with the user behavior data.
Preferably, the specific step of step 2 includes:
step 2.1, acquiring all DOMTrees through an HTML tag;
step 2.2, further traversing DOM elements, and obtaining unique identifiers of the DOM elements by tracking DOMTreee element links;
and 2.3, monitoring all behaviors of the user through unique identification of DOM elements, and obtaining relevant operation statistical data of the user.
Preferably, the specific step of step 3 includes:
step 3.1, counting relevant operation data of each user on the page, and transmitting the statistical data to a background server in an encryption mode;
and 3.2, carrying out bidirectional encryption on the statistical data at the same time and transmitting the statistical data to the DPI analysis log of the operator.
Preferably, the step 3.1 specifically includes:
step 3.1.1, recording the complete operation behavior sequence of a user through operation flow record;
and 3.1.2, submitting statistical data to a data collection service by collecting user browsing behaviors.
Preferably, the step 3.2 specifically includes:
step 3.2.1, encrypting the important data by using an asymmetric encryption algorithm for the statistical data;
step 3.2.2, attaching a section of bidirectional encrypted http character string to the whole network request;
and 3.2.3, performing transparent identification by using an equipment fingerprint technology and transparent transmitting the transparent identification to the DPI analysis log of the operator.
Preferably, the specific step of step 4 includes:
step 4.1, for the returned statistical data, performing multi-dimensional user advertisement browsing behavior aggregation analysis based on business processing by using a Flink stream processing mechanism and writing in OLAP solutions such as elastic search;
and 4.2, analyzing the transparent transmission data to obtain a multi-dimensional user behavior broad table.
Preferably, the step 4.2 specifically includes:
step 4.2.1, deep cleaning and excavating are carried out on the DPI by using a Hadoop ecological ring technology, and original behavior data of a user are analyzed;
and 4.2.2, generating a multi-dimensional user behavior broad table based on the business analysis on the data by using a Spark computing engine.
Preferably, the specific steps of the step 5 include:
step 5.1, generating a device unique ID by acquiring related device information based on a device fingerprint technology;
and 5.2, merging the multi-dimensional user behavior broad table and the aggregation analysis result to generate the cross-equipment fingerprint.
Preferably, in the step 6, a link streaming calculation engine is used to perform joint calculation of massive multidimensional user behavior data and real-time user advertisement browsing behaviors, so as to perform targeted advertisement delivery of system big data user behaviors.
According to the scheme, the method for encrypting the embedded point and the transparent tracking of the online advertisement DPI can greatly expand the application value and the space of the embedded point of the online advertisement and subsequent data analysis, and provides a safe and accurate advertisement monitoring method which can perform data communication and depth analysis on users with specific advertisement browsing, clicking, converting and other actions on the premise of legal and compliance. The invention solves the problems that the prior art cannot accurately orient advertisement behavior users, cannot communicate with other user characteristic data and cannot process deeper analysis requirements, has obvious action effect and is suitable for wide popularization.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a method for encryption embedded point and transparent tracking of an online advertisement DPI according to an embodiment of the present invention;
FIG. 2 is a second block diagram of a method for encryption embedded point and transparent tracking of an online advertisement DPI according to an embodiment of the present invention;
FIG. 3 is a process block diagram III of a method for encryption embedded point and transparent tracking of an online advertisement DPI according to an embodiment of the present invention;
FIG. 4 is a block diagram illustrating a method for encryption embedded point and transparent tracking of an online advertisement DPI according to an embodiment of the present invention;
FIG. 5 is a process block diagram fifth of a method for encryption embedded point and transparent tracking of an online advertisement DPI according to an embodiment of the present invention;
fig. 6 is a process block diagram sixth of a method for encryption embedded point and transparent tracking of an online advertisement DPI according to an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1 to 6, an embodiment of a method for encrypting a buried point and transparent tracking an online advertisement DPI according to the present invention will now be described.
The data embedding point is an important mode of data acquisition, and is mainly used for recording and collecting operation behaviors of a terminal user, the basic principle is that an acquired SDK code is deployed at the terminal, when the behaviors of the user meet certain conditions, such as entering a certain page, clicking a certain button and the like, the recording and the storage are automatically triggered, and then the data can be collected and transmitted to a terminal provider or request data in the service using process of the user is collected through a back end.
After the terminal provider collects the buried data, the terminal provider can obtain some basic indexes for measuring the state of the product, such as active, reserved, newly added and other large disc data through processing such as big data processing, data statistics, data analysis, data mining and the like, so that the state of the product is obtained.
The online advertisement embedded point is one of the most important application scenes of the data embedded point technology, and refers to a method for acquiring related data accessed by a user advertisement by embedding a data acquisition code in an advertisement page. Such as which button the user clicked, the order of hopping between advertising pages, dwell time, etc., which are the primary sources from which data analysis will be performed later.
The method for encrypting the embedded point and the transparent tracking of the online advertisement DPI comprises the following specific steps:
s1, accessing an SDK in an advertisement page, and collecting user operation behaviors, wherein the user operation behaviors comprise page access times, page stay time and button click times;
s1, uniformly accessing an SDK (software development kit) in a place needing to monitor statistics in an advertisement page, wherein the SDK comprises N lines of codes, burying the advertisement page, asynchronously downloading a core code by the SDK according to client customization (default is used when the client does not customize), ensuring that the uniform SDK has small volume, not influencing page loading, and not requiring business-level participation in updating the core code. The data that need to be monitored for statistics include: various user operation behavior data such as page access times, page stay time, button click times and the like.
SDKs (software development kits) are typically a collection of development tools that some software engineers create application software for a particular software package, software framework, hardware platform, operating system, etc.
S2, the SDK globally monitors the HTML tag, and when a user enters the advertisement page to perform various operations, a corresponding monitoring statistical mechanism is triggered to obtain relevant operation statistical data of the user;
s2, the specific steps include:
s2.1, acquiring all DOMTrees through a document. GetElementsByTagName ('body') type tag in the HTML tag;
s2.2, traversing various DOM elements further, and obtaining unique identifiers of the DOM elements by tracking DOMTreee element links, wherein the various DOM elements comprise HEAD, TITLE, BODY;
s2.3, monitoring all behaviors of the user through unique identification of DOM elements.
HTML (hypertext markup language) is an markup language that includes a series of tags by which document formats on a network can be unified to link discrete Internet resources into a logical entity.
DOM (document object model), DOM (document object model) refers to an HTMLtree tree structure and a corresponding access method generated by parsing an HTML page through the DOM.
S3, encrypting the returned statistical data, and simultaneously encrypting and transmitting the statistical data to an operator DPI analysis log;
s3.1, counting relevant operation data of each user on the page, and transmitting the statistic data to a background server in an encryption mode.
The S3.1 specifically comprises the following steps:
s3.1.1, recording the complete operation behavior sequence of the user through operation flow record;
s3.1.2, submitting statistics to a data collection service via asynchronous GET requests by collecting user browsing behavior.
S3.2, in the whole network interaction process of the user, the statistical data are encrypted in two directions simultaneously and transmitted to the DPI analysis log of the operator;
the specific steps of S3.2 include:
s3.2.1 for statistical data, encrypting the important data using an asymmetric encryption algorithm, wherein the important data comprises scoring data for media-user packets;
s3.2.2, synchronously attaching a section of bidirectional encrypted http character string to the whole network request in the whole network interaction process of the user;
s3.2.3, using the device fingerprint technology to make transparent identification and transparent transmission to the DPI analysis log of the operator.
DPI (deep packet inspection) is a deep inspection technology based on data packets, and performs deep inspection on different network application layer loads, and determines validity of the packet by detecting the payload of the packet.
http (hypertext transfer protocol) is a simple request-response protocol that typically runs on top of TCP, specifying what messages a client might send to a server and what responses get.
A device fingerprint refers to a device characteristic or unique device identification that may be used to uniquely identify the device.
S4, carrying out multidimensional user advertisement browsing behavior aggregation analysis on the returned statistical data, and analyzing the encrypted and transmitted data to generate a multidimensional user behavior broad table;
s4, the specific steps include:
s4.1, for the returned statistical data, carrying out multi-dimensional user advertisement browsing behavior aggregation analysis based on business processing by using a Flink stream processing mechanism and writing in OLAP solutions such as elastic search;
the Flink stream processing is a stream processing application that can help users implement stateful.
The elastiscearch is a search server. The cloud computing system is a popular enterprise-level search engine, is used in cloud computing, can achieve real-time search, is stable, reliable and quick, and is convenient to install and use.
OLAP (online analytical processing) is a software technology that enables analysts to quickly, consistently, interactively view information from various aspects for purposes of deep understanding of data.
S4.2, analyzing the transparent transmission data to obtain the multi-dimensional user behavior broad table.
The specific steps of S4.2 include:
s4.2.1, deep cleaning and mining are carried out on the DPI by using a Hadoop ecological circle related technology, and user original behavior data are analyzed, wherein the Hadoop ecological circle related technology comprises HDFS, hive, mapreduce, spark and the like;
s4.2.2 an iterative distributed computation engine using Spark generates a multi-dimensional user behavior broad table based on business analysis of the data.
Hadoop is a distributed system infrastructure. The user may develop the distributed program without knowing the details of the distributed underlying layer. And the power of the clusters is fully utilized to perform high-speed operation and storage. Has the characteristics of reliability, high efficiency and scalability.
HDFS (Hadoop distributed file system) refers to a distributed file system designed to operate on general purpose hardware.
Hive is a data warehouse tool based on Hadoop for data extraction, transformation, and loading, which is a mechanism that can store, query, and analyze large-scale data stored in Hadoop.
Mapreduce is a programming model for parallel operation of large-scale data sets (greater than 1 TB).
Spark is a fast and versatile computing engine designed for large-scale data processing.
S5, data are opened and restored by combining the multidimensional user behavior broad table and the aggregation analysis result;
the server data and the operator data are restored by the encryption restoration system deployed on the operator data processing platform in a bidirectional decryption mode, so that accurate orientation of users is realized in the operator data service platform, and a foundation is laid for subsequent advertisement effect and crowd analysis based on various labels of operators.
S5, the specific steps include:
s5.1, generating a device unique ID (identity) by acquiring related device information (an operating system, a browser version number, screen resolution, a browser plug-in and the like) based on a device fingerprint technology;
s5.2, merging by combining the multidimensional user behavior broad table with the result of the aggregation analysis, merging according to preference habits and the like, and generating a cross-equipment fingerprint.
ID (Identitydocument) is an abbreviation for various proprietary words such as identification number, account number, unique code, proprietary number, industrial design, national abbreviation, legal words, general account, decoder, software company, etc.
S6, combining the user behavior data, and performing subsequent release analysis by using a Flink stream type calculation engine.
The method specifically comprises the steps of using a Flink streaming computing engine to perform joint computation of massive multidimensional user behavior data and real-time user advertisement browsing behaviors, and further performing targeted advertisement delivery of system big data user behaviors.
The DPI encryption buried point and transparent transmission tracking method for the online advertisement can realize accurate identification and tracking of advertisement user individuals on the premise of fully ensuring data safety and permission of laws and regulations through DPI transparent transmission of bidirectional encryption, and allows user data and other labels with different dimensions to be communicated in an operator data capacity open platform so as to carry out deep advertisement attribution analysis, and further complete a more accurate user advertisement orientation algorithm through continuous optimization. The advertisement click user is identified and tracked by combining with DPI data (telecom operator deep data packet inspection log data), and the whole advertisement conversion effect is obviously improved by the application of the method.
Exemplary: advertisement in the automobile industry is put in and monitored, and the experimental time is from 1 day of 9 months in 2019 to 31 days of 10 months in 2019 for two months. The specific implementation steps are that an experimental group and a control group are set, wherein the experimental group uses the method of on-line advertisement DPI encryption burial point and transparent transmission tracking to monitor user behavior and put advertisements at fixed points, the control group uses the existing advertisement burial point monitoring method to monitor user behavior and put advertisements, and under the condition that other conditions are the same, the judgment standards such as advertisement jump rate, click rate and comprehensive conversion rate are observed to obtain the following experimental results:
1000 ten thousand times of advertisement pushing is carried out on the same network user side in the same area, the total access amount generated by pushing the advertisement page by the experimental group is 745.1 ten thousand times, the access amount which is separated when only the advertisement page is accessed is 291.4 ten thousand times, and the advertisement jump rate is 39.1%; the number of times a certain content on the advertisement page is displayed is 286.3 ten thousand times, the number of times the content is clicked is 17.1 ten thousand times, and the click rate is 6%; the tasks of the ad page were completed 10.5 ten thousand times, resulting in a comprehensive conversion of 1.41%.
The total access amount generated by pushing the control group to obtain the advertisement page is 697.8 ten thousand times, the access amount which is separated when only the advertisement page is accessed is 287.3 ten thousand times, and the jump-out rate of the advertisement is about 41.2%; the number of times a certain content on the advertisement page is displayed is 257.4 ten thousand times, the number of times the content is clicked is 13.4 ten thousand times, and the click rate is 5.21%; the number of times the ad page tasks were completed was 7.6 ten thousand times, resulting in a comprehensive conversion of 1.09%.
By comparison, finally, the conclusion is drawn: compared with the control group, the advertisement jumping rate of the experimental group is optimized and improved by about 5%; click rate optimization is improved by about 15%; the comprehensive conversion rate is improved by about 30 percent.
According to the data, the advertisement delivery data analysis is performed by the method, the advertisement delivery data can be accurately directed to independent users, and the opening analysis is performed with third party data, so that the advertisement crowd orientation can be continuously optimized and improved, and the cost reduction and synergy effects of the advertisement delivery process are quite remarkable.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. What is not described in detail in the embodiments of the present invention belongs to the prior art known to those skilled in the art.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (3)
1. The method for encrypting embedded points and transparent transmission tracking of the online advertisement DPI is characterized by comprising the following specific steps:
step 1, accessing an SDK in an advertisement page, and collecting user operation behaviors, wherein the user operation behaviors comprise page access times, page stay time and button click times;
step 2, the SDK globally monitors the HTML tag, and when a user enters an advertisement page to operate, a corresponding monitoring statistical mechanism is triggered to obtain relevant operation statistical data of the user;
step 3, encrypting the returned statistical data, and simultaneously encrypting and transmitting the statistical data to an operator DPI analysis log;
step 4, carrying out multidimensional user advertisement browsing behavior aggregation analysis on the returned statistical data, and analyzing the encrypted and transmitted data to generate a multidimensional user behavior wide table;
step 5, data open reduction is carried out by combining the multidimensional user behavior broad table with the result of aggregation analysis;
step 6, combining user behavior data, and performing subsequent release analysis by using a Flink stream type calculation engine;
the specific steps of the step 3 include:
step 3.1, counting relevant operation data of each user on the page, and transmitting the statistical data to a background server in an encryption mode;
step 3.2, carrying out bidirectional encryption on the statistical data at the same time and transmitting the statistical data to an operator DPI analysis log;
the step 3.1 specifically comprises the following steps:
step 3.1.1, recording the complete operation behavior sequence of a user through operation flow record;
step 3.1.2, submitting statistical data to a data collection service by collecting user browsing behaviors;
the step 3.2 specifically comprises the following steps:
step 3.2.1, encrypting the important data by using an asymmetric encryption algorithm for the statistical data;
step 3.2.2, attaching a section of bidirectional encrypted http character string to the whole network request;
step 3.2.3, using the equipment fingerprint technology to make a transparent identification and transmitting the transparent identification to the DPI analysis log of the operator;
the specific steps of the step 4 include:
step 4.1, for the returned statistical data, performing multidimensional user advertisement browsing behavior aggregation analysis based on an OLAP solution which uses a Flink stream processing mechanism to process business and writes in an elastic search;
step 4.2, analyzing the transparent transmission data to obtain a multi-dimensional user behavior broad table;
the specific steps of the step 4.2 comprise:
step 4.2.1, deep cleaning and excavating are carried out on the DPI by using a Hadoop ecological ring technology, and original behavior data of a user are analyzed;
step 4.2.2, generating a multi-dimensional user behavior wide table based on business analysis for the user original behavior data by using a Spark computing engine;
the specific steps of the step 5 include:
step 5.1, generating a device unique ID by acquiring related device information based on a device fingerprint technology;
and 5.2, merging the multi-dimensional user behavior broad table and the aggregation analysis result to generate the cross-equipment fingerprint.
2. The method for encryption embedded point and transparent tracking of online advertisement DPI according to claim 1, wherein the specific steps of step 2 include:
step 2.1, acquiring all DOM Tree through an HTML tag;
step 2.2, further traversing DOM elements, and obtaining unique identifiers of the DOM elements by tracking links of the DOM Tree elements;
and 2.3, monitoring all behaviors of the user through unique identification of DOM elements, and obtaining relevant operation statistical data of the user.
3. The method for encryption embedded point and transparent tracking of online advertisement DPI according to claim 2, wherein step 6 is specifically to use a link stream calculation engine to perform a joint calculation of massive multidimensional user behavior data and real-time user advertisement browsing behaviors, so as to perform targeted advertisement delivery of system big data user behaviors.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110070387A (en) * | 2009-12-18 | 2011-06-24 | 에스케이 텔레콤주식회사 | Return on investment of advertisement measuring system using a deep packet inspection and measuring method thereof |
| CN103916293A (en) * | 2014-04-15 | 2014-07-09 | 浪潮软件股份有限公司 | Method for monitoring and analyzing website user behaviors |
| CN105553940A (en) * | 2015-12-09 | 2016-05-04 | 北京中科云集科技有限公司 | Safety protection method based on big data processing platform |
| CN106161015A (en) * | 2016-09-29 | 2016-11-23 | 长春大学 | A kind of quantum key distribution method based on DPI |
| CN106228391A (en) * | 2016-07-14 | 2016-12-14 | 精硕世纪科技（北京）有限公司 | The method and system of monitoring of the advertisement |
| CN108416620A (en) * | 2018-02-08 | 2018-08-17 | 杭州浮云网络科技有限公司 | A kind of intelligent social advertisement launching platform of the representation data based on big data |
| CN109903068A (en) * | 2017-12-11 | 2019-06-18 | 杭州掌优科技有限公司 | A kind of precise quantification and accurate statistics launch the advertisement distributing system and method for effect |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8768766B2 (en) * | 2005-03-07 | 2014-07-01 | Turn Inc. | Enhanced online advertising system |
| US20100313009A1 (en) * | 2009-06-09 | 2010-12-09 | Jacques Combet | System and method to enable tracking of consumer behavior and activity |
| US20180047048A1 (en) * | 2016-08-15 | 2018-02-15 | Gemius S.A. | Digital advertising campaign monitoring system, apparatus, and method |
-
2020
- 2020-08-11 CN CN202010799068.6A patent/CN111882368B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20110070387A (en) * | 2009-12-18 | 2011-06-24 | 에스케이 텔레콤주식회사 | Return on investment of advertisement measuring system using a deep packet inspection and measuring method thereof |
| CN103916293A (en) * | 2014-04-15 | 2014-07-09 | 浪潮软件股份有限公司 | Method for monitoring and analyzing website user behaviors |
| CN105553940A (en) * | 2015-12-09 | 2016-05-04 | 北京中科云集科技有限公司 | Safety protection method based on big data processing platform |
| CN106228391A (en) * | 2016-07-14 | 2016-12-14 | 精硕世纪科技（北京）有限公司 | The method and system of monitoring of the advertisement |
| CN106161015A (en) * | 2016-09-29 | 2016-11-23 | 长春大学 | A kind of quantum key distribution method based on DPI |
| CN109903068A (en) * | 2017-12-11 | 2019-06-18 | 杭州掌优科技有限公司 | A kind of precise quantification and accurate statistics launch the advertisement distributing system and method for effect |
| CN108416620A (en) * | 2018-02-08 | 2018-08-17 | 杭州浮云网络科技有限公司 | A kind of intelligent social advertisement launching platform of the representation data based on big data |
Non-Patent Citations (3)
| Title |
|---|
| 互联网广告流式处理系统的设计与实现;郑国英 等;《微型电脑应用》;第35卷(第06期);第85-88页 * |
| 广告投放数据管理平台的设计与实现;石路遥;《中国优秀硕士学位论文全文数据库 信息科技辑》(第09期);第1-74页 * |
| 浏览器用户行为识别技术的研究;蒋鹏飞;《中国优秀硕士学位论文全文数据库 信息科技辑》(第04期);第1-63页 * |
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